The invention is directed to a real time prediction device for use in combination with a human being where the human being issues data.
In many applications there is an interest in producing one or more alarms based on the state of a human being, where the human being issues data, which can typically be acquired via appropriate sensors. Following capture, the data is generally transformed in some sort of a signal, typically electronic. One or more such signals may then be combined into other signals that are not directly generated by the human being. These signals are typically obtained by computation but are related to the data issued by the human being.
An example of such an application is a device to prevent a driver of a vehicle from falling asleep at the wheel or from having an accident due to drowsiness or, equivalently, to somnolence. Here, the driver is obviously the human being of interest. The signal related to the data might be a level of drowsiness of the driver obtained in some way, e.g. via polysomnography (PSG) or via photooculography (POG). The device would issue an alarm when the level of drowsiness reaches a level deemed “reference level”. The alarm may be, among others, by audible, visible, olfactory, and/or vibratory means.
US2004/0236235 describes a human condition detection system for detecting the transition from an active sate to a sleep state. A bio-signal analysis means includes a bio-signal peak value detection means for detecting a peak value for each cycle of an original waveform of bio-signal data, and a power value calculation means for calculating a difference between the peak value at an upper limit side and the peak value at a lower limit side for every prescribed time range. A slide calculation is performed to determine if a sudden drop state appears that is indicative of falling asleep. This publication predicts the human condition at the present moment.
While it is obviously important to be able to determine the level of drowsiness of a driver and to make predictions about this level in the case of conventional vehicles and driving, it is also important to do so in the case of self-driving vehicles. Indeed, the automatic driving system may decide to take over from the driver if it determines that the level of drowsiness of the driver becomes too high, or the automatic driving system may need to decide whether it is safe to hand over the “manual” driving to the driver.
In the field of predicting a future state based on past events, one often assumes that the input, i.e. the data as issued by the human being, is a realization of a moving average (MA) random process, or an autoregressive (AR) random process, or an autoregressive moving average (ARMA) random process, or similar traditional random processes. Equivalently, one can say that the input is modeled by an MA, AR random process, or ARMA random process. These techniques allow one to estimate the future values of a signal as linear combinations of previous (and known) values of the signal, typically in a window. The prediction process is then implemented via a linear-time invariant (LTI) system.
Two disadvantages of such methods is that the MA, AR, and ARMA random process models may not be accurate for the application of interest and/or that they usually require significant computational capability in actual use as further described below.